The ticks_w_weather dataset contains the following
variables:
site_id: The four-letter NEON site
codedate: The first day of the MMWR week for this
rowjd: The Julian date calculated from
the date columnmmwr_year: The year of the MMWR
weekmmwr_week: The week number of the MMWR
weekmean_temp: The mean temperature of the
current MMWR week. Calculated by taking the mean of daily mean
temperaturesmin_temp: The minimum temperature of
the current MMWR week. Calculated by taking the minimum of daily minimum
temperaturesmax_temp: The maximum temperature of
the current MMWR week. Calculated by taking the maximum of daily maximum
temperaturesmean_rh: The mean relative humidity
(%) of the current MMWR week. Calculated by taking the mean of daily
mean RH valuesrh_min: The minimum relative humidity
(%) of the current MMWR week. Calculated by taking the minimum of daily
minimum RH valuesrh_max: The maximum relative humidity
(%) of the current MMWR week. Calculated by taking the maximum of daily
maximum RH valuesmean_vpd: The mean vapor pressure
deficit for the current MMWR week. Calculated by taking the mean of
daily mean VPD valuesmean_precip_mm: The mean daily
precipitation (mm) for the current MMWR week. Calculated by taking the
mean of daily precipitation sumssum_precip_mm: Sum of daily
precipitation (mm) for the current MMWR week. Calculated by summing the
daily precipitation sumsdd: The average daily degree days
accumulated over the current MMWR weekthirty_day_dd: The mean
30-day-rolling-sum of degree days for the current MMWR week. Calculated
by taking the mean of daily thirty-day rolling sumsdd_30d_rollsum_lag34: The mean
30-day-rolling-sum of degree days for the MMWR week 34 weeks previous.
To calculate: 1) Get thirty_day_dd calculations, 2) get the
34-week lagged values of those (i.e.,
lag(x = thirty_day_dd, n = 34L)), 3) average the values for
all days in the current MMWR weekdd_30d_rollsum_lag42: Same as
lag_thirty_day_dd_34wk, but 42 weeks previousdd_30d_rollsum_lag50: Same as
lag_thirty_day_dd_34wk, but 50 weeks previousdd_30d_rollsum_prev_week: This is the
thirty-day rolling sum of degree days from the last day of the
previous MMWR week. No averaging takes place. e.g, if today is Sunday,
then this is the 30-day rolling degree day sum of yesterday
(Saturday)cume_dd_prev_week: Similar to
dd_30d_rollsum_prev_week, except this is the cumulative
degree day count (starting Jan. 1) of the the last day of the
previous MMWR weekcume_cd_prev_winter: The total number
of “chill days” (see method below under Notes) from September
1st of the preceding year to March 31st of the current year. Values
before April 1st use the previous year’s accumulationsamblyomma_americanum: The density of
Amblyomma americanum ticks for the current MMWR week, reported
as ticks per 1600m2amam_filled: A version of the tick
count column above that has been gap filled using linear
interpolationtick_interp_flag: A flag column that
indicates whether the week’s value for amam_filled was
observed or interpolatedamam_4wk_rollmean_lag1: The four-week
rolling average of the interpolated tick count column, then lagged by
one weekmean_vpd_4wk_rollmean_lag1: The
four-week rolling average of the mean_vpd column, then
lagged by one weekamam_4wk_rollmean_lag50: The four-week
rolling average of the interpolated tick count column, then lagged by 50
weeksmean_vpd_4wk_rollmean_lag50: The
four-week rolling average of the mean_vpd column, then
lagged by 50 weeksNotes:
degree days
accumulated that day. Negative values (i.e., temps below 0C) do not
count towards this.chill days
accumulated that day. Negative values (i.e., temps above 0C) do not
count towards this.| site_id | date | jd | mmwr_year | mmwr_week | mean_temp | min_temp | max_temp | rh_min | rh_max | mean_vpd | mean_precip_mm | sum_precip_mm | dd | thirty_day_dd | dd_30d_rollsum_lag34 | dd_30d_rollsum_lag42 | dd_30d_rollsum_lag50 | dd_30d_rollsum_prev_week | cume_dd_prev_week | cume_cd_prev_winter | amblyomma_americanum | dd_rollsum_prev_week | amam_filled | tick_interp_flag | amam_4wk_rollmean_lag1 | mean_vpd_4wk_rollmean_lag1 | amam_4wk_rollmean_lag50 | mean_vpd_4wk_rollmean_lag50 | amam_lag1 | amam_lag2 | amam_lag3 | amam_lag4 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BLAN | 2015-04-19 | 109 | 2015 | 16 | 11.36429 | 1.25 | 26.55 | 25.6 | 100.0 | 0.7171429 | 4.6848571 | 32.794 | 11.36429 | 335.0286 | 101.1143 | 45.05714 | 28.60000 | 303.50 | 459.85 | 227.95 | 0.000000 | 107.65 | 0.000000 | original | 4.907976 | 0.8864286 | 4.907976 | 0.8864286 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| BLAN | 2015-04-26 | 116 | 2015 | 17 | 13.03571 | 2.85 | 23.15 | 28.6 | 96.9 | 0.7571429 | 3.3024286 | 23.117 | 13.03571 | 369.4929 | 135.1643 | 94.20714 | 39.95000 | 339.70 | 539.40 | 227.95 | NA | 79.55 | 3.271984 | interpolated | 4.907976 | 0.8864286 | 4.907976 | 0.8864286 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| BLAN | 2015-05-03 | 123 | 2015 | 18 | 20.17857 | 9.05 | 29.65 | 23.7 | 100.0 | 1.1057143 | 0.6911429 | 4.838 | 20.17857 | 414.6643 | 185.3143 | 131.07857 | 87.17857 | 383.65 | 630.65 | 227.95 | NA | 91.25 | 6.543967 | interpolated | 4.907976 | 0.8864286 | 4.907976 | 0.8864286 | 3.271984 | 0.000000 | 0.000000 | 0.000000 |
| BLAN | 2015-05-10 | 130 | 2015 | 19 | 19.76429 | 5.55 | 30.65 | 37.0 | 100.0 | 0.9657143 | 0.3840000 | 2.688 | 19.76429 | 470.0214 | 245.2286 | 175.57857 | 126.60714 | 442.75 | 771.90 | 227.95 | 9.815951 | 141.25 | 9.815951 | original | 4.907976 | 0.8864286 | 4.907976 | 0.8864286 | 6.543967 | 3.271984 | 0.000000 | 0.000000 |
| BLAN | 2015-05-17 | 137 | 2015 | 20 | 17.82143 | 5.05 | 31.35 | 29.7 | 100.0 | 0.8371429 | 0.0000000 | 0.000 | 17.82143 | 502.1643 | 285.6500 | 239.02857 | 167.07143 | 486.05 | 910.25 | 227.95 | NA | 138.35 | 9.877301 | interpolated | 4.907976 | 0.8864286 | 4.907976 | 0.8864286 | 9.815951 | 6.543967 | 3.271984 | 0.000000 |
| BLAN | 2015-05-24 | 144 | 2015 | 21 | 23.27857 | 14.15 | 30.75 | 31.7 | 96.3 | 1.2085714 | 0.6142857 | 4.300 | 23.27857 | 563.6286 | 339.3714 | 277.38571 | 232.33571 | 511.40 | 1035.00 | 227.95 | NA | 124.75 | 9.938650 | interpolated | 7.377301 | 0.9164286 | 4.907976 | 0.8864286 | 9.877301 | 9.815951 | 6.543967 | 3.271984 |
| BLAN | 2015-05-31 | 151 | 2015 | 22 | 18.64286 | 12.05 | 31.45 | 44.6 | 100.0 | 0.5071429 | 7.1425714 | 49.998 | 18.64286 | 605.2000 | 375.1643 | 335.02857 | 269.83571 | 595.70 | 1197.95 | 227.95 | 10.000000 | 162.95 | 10.000000 | original | 9.043967 | 1.0292857 | 4.907976 | 0.8864286 | 9.938650 | 9.877301 | 9.815951 | 6.543967 |
| BLAN | 2015-06-07 | 158 | 2015 | 23 | 24.45000 | 13.85 | 33.35 | 36.2 | 100.0 | 1.2314286 | 1.3054286 | 9.138 | 24.45000 | 609.3500 | 423.9071 | 369.49286 | 329.85714 | 597.00 | 1328.45 | 227.95 | 19.393939 | 130.50 | 19.393939 | original | 9.907975 | 0.8796429 | 4.907976 | 0.8864286 | 10.000000 | 9.938650 | 9.877301 | 9.815951 |
| BLAN | 2015-06-14 | 165 | 2015 | 24 | 25.69286 | 19.55 | 32.15 | 45.7 | 92.2 | 1.1871429 | 4.3777143 | 30.644 | 25.69286 | 650.7429 | 475.8786 | 414.66429 | 363.21429 | 632.95 | 1499.60 | 227.95 | NA | 171.15 | 11.265597 | interpolated | 12.302473 | 0.9460714 | 4.907976 | 0.8864286 | 19.393939 | 10.000000 | 9.938650 | 9.877301 |
| BLAN | 2015-06-21 | 172 | 2015 | 25 | 23.82143 | 15.95 | 34.35 | 40.8 | 100.0 | 1.0314286 | 0.3840000 | 2.688 | 23.82143 | 698.1357 | 506.6500 | 470.02143 | 406.22143 | 673.95 | 1679.45 | 227.95 | 3.137255 | 179.85 | 3.137255 | original | 12.649547 | 1.0335714 | 4.907976 | 0.8864286 | 11.265597 | 19.393939 | 10.000000 | 9.938650 |
Timeseries
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Tick count line plot:
Raw tick counts overlaid on interpolation:
tar_visnetwork()
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